ML Solutions Architect – Data Agents

November 25

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JetBrains

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B2B • SaaS • Artificial Intelligence

JetBrains is a software company that builds professional developer tools and integrated development environments (IDEs) — including IntelliJ IDEA, PyCharm, WebStorm, Rider, and others — plus team and CI/CD tools like TeamCity, YouTrack, Datalore, and code-quality services. The company also offers AI-powered developer features (Junie, AI Assistant, AI Enterprise), a marketplace for plugins, educational offerings (JetBrains Academy, courses, free licenses for students/teachers), and enterprise services for managing developer tooling at scale. JetBrains focuses on improving developer productivity, collaboration, and code quality for individual developers and organizations worldwide.

1001 - 5000 employees

Founded 2000

🤝 B2B

☁️ SaaS

🤖 Artificial Intelligence

📋 Description

• Designing and implementing ML- and LLM-based solutions for automated context discovery, enrichment, and evaluation. • Architecting systems that connect internal data, metrics, documents, and tools into robust contextual foundations for agents. • Providing technical expertise in prompt engineering, RAG architectures, model selection, and inference optimization. • Defining and maintaining metrics and benchmarks for context quality, relevance, and downstream task success. • Collaborating with product and engineering teams to identify customer feedback and shape the platform roadmap. • Mentoring ML engineers and researchers, driving best practices in experimentation, architecture, and evaluation.

🎯 Requirements

• At least five years of experience in ML/AI systems, with at least two years focused on LLMs and generative AI. • A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches. • Hands-on experience with: • Prompt engineering and LLM pipeline design, including evaluation. • Agentic frameworks such as LangChain, LlamaIndex, LangSmith, smolagents, or an equivalent. • Vector databases and retrieval-augmented generation (RAG) patterns. • Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models. • Strong Python skills. • Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences. • Proficiency in English, both written and verbal.